Scale invariant version of the original PNN proposed by Specht (1990) <doi:10.1016/0893-6080(90)90049-q> with the added functionality of allowing for smoothing along multiple dimensions while accounting for covariances within the data set. It is written in the R statistical programming language. Given a data set with categorical variables, we use this algorithm to estimate the probabilities of a new observation vector belonging to a specific category. This type of neural network provides the benefits of fast training time relative to backpropagation and statistical generalization with only a small set of known observations.
Package details |
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| Author | Romin Ebrahimi [aut, cre] |
| Maintainer | Romin Ebrahimi <romin.ebrahimi@utexas.edu> |
| License | GPL (>= 2) |
| Version | 1.3.0 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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